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About This Role
Software Engineer — AI Evaluation & Automation
Location: 100% Remote
Duration: Long-term contract
Strongly needed experience in Benchmarking AI that writes code.
Role Overview Help build and scale the tooling we use to measure how well AI-powered software development tools actually perform. You'll develop evaluation harnesses, automate benchmark runs, and help make sure the results we produce are reproducible and hold up to scrutiny. This is an engineering role, but a lot of the work is about getting the measurement right, not just automating it.
Key Responsibilities
- Build and integrate evaluation harnesses and automation for software development use cases, including turning real engineering artifacts like merged pull requests into repeatable benchmark tasks.
- Build versioned, repeatable processes to evaluate AI tools, models, and harnesses, with reproducible run environments (pinned dependencies, containerized runs, isolated worktrees) so results stay comparable over time.
- Validate and calibrate evaluation approaches against human judgment, so scores are consistent and correct rather than just repeatable.
- Support execution-based benchmarking across quality, productivity, and efficiency measures, including cost and latency.
- Analyze results across repeated runs, looking at variance, failure patterns, and cost per outcome, and find ways to make the workflows more reliable and more automated.
- Work with engineering and data teams to improve the tooling, and document how the evaluations work and what they found for both technical and leadership audiences.
Required Skills & Experience
- Strong software engineering background, with real experience building automation, developer tooling, or test and validation systems.
- Hands-on work with AI-powered coding tools and agentic applications, such as Claude Code, Devin, or OpenCode.
- Proficient in Python, and comfortable in at least one of Java, JavaScript, or a similar language.
- Solid working knowledge of Git, including how branches, history, and working trees behave, and of containerization with Docker.
- Experience with APIs, development environments, CI/CD pipelines, and typical engineering workflows.
- Some familiarity with how AI, LLM, or agent evaluation works and where it goes wrong, such as why a judge can be consistent but still wrong, why a single run can mislead, and how benchmark contamination happens.
- Able to troubleshoot technical problems, think clearly about whether a measurement is valid, and analyze results carefully.
Preferred Experience
- Experience designing benchmarks or evaluations for software systems, especially execution-based grading that verifies against tests.
- Familiarity with LLM-as-judge or agent-as-judge approaches, and how to check them against human raters.
- Experience with build-system-aware test selection, such as Bazel or mapping changed files to the tests that cover them.
- Experience building reproducible test environments and managing versioned evaluation datasets.
- Comfortable writing up methodology and results for engineering leadership.
Should you be interested, please send me your resume along with the following details ASAP to [email protected]:
Full Name: Current Location: Hourly rate on W2: Work Authorization: Earliest Available date to start: Date and times available to interview:
Pay: $60.00 - $70.00 per hour
Expected hours: 40.0 per week
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Life insurance
Work Location: Remote
Salary Context
This $124K-$145K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $193K across 172 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Invovia, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($135K) sits 39% below the category median. Disclosed range: $124K to $145K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Invovia AI Hiring
Invovia has 1 open AI role right now. They're hiring across AI Software Engineer. Based in US. Compensation range: $145K - $145K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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